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                <h1 class="title-article">Hive ORC和Parquet</h1>
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<p>相比传统<a title="MySQL知识库" class="replace_word" href="http://lib.csdn.net/base/14" rel="nofollow" style="color:rgb(223,52,52);font-weight:bold;">数据库</a>的行式存储引擎，列式存储引擎具有更高的压缩比，更少的IO操作，尤其是在数据列很多，但每次操作仅针对若干列进行查询和计算的情景，列式存储引擎的性价比更高。</p>
<p>目前在开源实现中，最有名的列式存储引擎莫过于Parquet和ORC，并且他们都是Apache的顶级项目，在数据存储引擎方面发挥着重要的作用。</p>
<p>本文将重点讲解ORC文件存储格式，Parquet暂不深入说明，后续抽时间整理。</p>
<p> </p>
<p><strong>1、</strong>Apache Parquet</p>
<p> </p>
<p>源自于google Dremel系统，Parquet相当于GoogleDremel中的数据存储引擎，而Apache顶级开源项目Drill正是Dremel的开源实现。</p>
<p>Apache Parquet 最初的设计动机是存储嵌套式数据，比如Protocolbuffer，thrift，json等，将这类数据存储成列式格式，以方便对其高效压缩和编码，且使用更少的IO操作取出需要的数据，这也是Parquet相比于ORC的优势，它能够透明地将Protobuf和thrift类型的数据进行列式存储，在Protobuf和thrift被广泛使用的今天，与parquet进行集成，是一件非容易和自然的事情。除了上述优势外，相比于ORC, Parquet没有太多其他可圈可点的地方，比如它不支持update操作（数据写成后不可修改），不支持ACID等。</p>
<p>Hive中创建表时使用Parquet数据存储格式：</p>
<p>create table parquet_table(id int,name string) stored as parquet;</p>
<p>    </p>
<p>2、Apache ORC</p>
<p> </p>
<p>ORC(OptimizedRow Columnar) 文件格式存储源自于RC（RecordColumnar File）这种存储格式，RC是一种列式存储引擎，对schema演化（修改schema需要重新生成数据）支持较差，而ORC是对RC改进，但它仍对schema演化支持较差，主要是在压缩编码，查询性能方面做了优化。RC/ORC最初是在Hive中得到使用，最后发展势头不错，独立成一个单独的项目。Hive 1.x版本对事务和update操作的支持，便是基于ORC实现的（其他存储格式暂不支持）。ORC发展到今天，已经具备一些非常高级的feature，比如支持update操作，支持ACID，支持struct，array复杂类型。你可以使用复杂类型构建一个类似于parquet的嵌套式数据<a title="大型网站架构知识库" class="replace_word" href="http://lib.csdn.net/base/16" rel="nofollow" style="color:rgb(223,52,52);font-weight:bold;">架构</a>，但当层数非常多时，写起来非常麻烦和复杂，而parquet提供的schema表达方式更容易表示出多级嵌套的数据类型。</p>
<p>Hive中创建表时使用ORC数据存储格式：</p>
<p>create table orc_table (id int,name string) stored as orc;</p>
<p> </p>
<p>3、Parquet与ORC对比</p>
<p> </p>
<table border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top">
<p align="center"> </p>
</td>
<td valign="top">
<p align="center">Parquet</p>
<p align="center">http://parquet.apache.org</p>
</td>
<td valign="top">
<p align="center">Orc</p>
<p align="center">http://orc.apache.org</p>
</td>
</tr><tr><td valign="top">
<p align="center">发展状态</p>
</td>
<td width="379" valign="top" colspan="2">
<p align="center">目前都是Apache开源的顶级项目，列式存储引擎</p>
</td>
</tr><tr><td valign="top">
<p align="center">开发语言</p>
</td>
<td width="379" valign="top" colspan="2">
<p align="center"><a title="Java EE知识库" class="replace_word" href="http://lib.csdn.net/base/17" rel="nofollow" style="color:rgb(223,52,52);font-weight:bold;">Java</a></p>
</td>
</tr><tr><td valign="top">
<p align="center">主导公司</p>
</td>
<td valign="top">
<p align="center">Twitter/Cloudera</p>
</td>
<td valign="top">
<p align="center">Hortonworks</p>
</td>
</tr><tr><td valign="top">
<p align="center">列编码</p>
</td>
<td valign="top">
<p align="center">支持多种编码，字典，RLE，Delta等</p>
</td>
<td valign="top">
<p align="center">支持主流编码，与Parquet类似</p>
</td>
</tr><tr><td valign="top">
<p align="left">                        ACID</p>
</td>
<td valign="top">
<p align="center">不支持</p>
</td>
<td valign="top">
<p align="center">支持ACID事务</p>
</td>
</tr><tr><td valign="top">
<p align="center">修改操作(update,delete)</p>
</td>
<td valign="top">
<p align="center">不支持</p>
</td>
<td valign="top">
<p align="center">支持</p>
</td>
</tr><tr><td valign="top">
<p align="center">支持索引</p>
<p align="center">(统计信息)</p>
</td>
<td valign="top">
<p align="center">粗粒度索引</p>
<p align="left">block/group/chunk级别统计信息</p>
</td>
<td valign="top">
<p align="center">粗粒度索引</p>
<p align="left">file/stripe/row级别统计信息，不能精确到列建立索引</p>
</td>
</tr><tr><td valign="top">
<p align="center">查询性能</p>
</td>
<td width="379" valign="top" colspan="2">
<p align="center">Orc性能更高一点</p>
</td>
</tr><tr><td valign="top">
<p align="center">压缩比</p>
</td>
<td width="379" valign="top" colspan="2">
<p align="center">Orc压缩比更高</p>
</td>
</tr></tbody></table><p> </p>
<p>下面看一张图，可以比对一下压缩率：</p>
<p></p>
<p> </p>
<p> </p>
<p><strong>4、</strong>ORC</p>
<p>使用ORC文件格式可以提升Hive读、写与处理数据的性能。</p>
<p>一个ORC文件包含多个<strong>stripes</strong>（<span style="color:#ff0000;">每个<strong>stripes</strong>由多组行数据组成的</span>）<strong>，</strong>一个包含辅助信息的<strong>file footer</strong>。</p>
<p>在文件的结尾，一个<strong>postscript</strong>保存着压缩参数及被压缩的<strong>footer</strong>的长度。</p>
<p><span style="color:#ff0000;">一个<strong>stripes</strong>缺省大小是250MB</span>，其大小可以扩展的长度只受HDFS的约束。</p>
<p><strong>file footer包</strong>含文件中的一个记录<strong>stripes信息</strong>的列表、每个<strong>stripes</strong>中行的数目及每个列的数据类型，它也包含列级的聚合结果：count, min, max, and sum。</p>
<p>我们通过使用<strong>hive --orcfiledump来进行分析ORC存储文件</strong>，就可以看到这些信息：</p>
<p>hive --orcfiledump &lt;path_to_file&gt;</p>
<p>示例：</p>
<p>hive --orcfiledump /user/hive/warehouse/helloworld.db/test_orcfile/part-00271</p>
<p></p>
<p> </p>
<p>对于Hive 1.1，查看ORC File文件中的内容可以使用如下的方式：</p>
<p>hive --orcfiledump -d &lt;path_to_file&gt;</p>
<p>示例：</p>
<p>hive --orcfiledump -d /user/hive/warehouse/helloworld.db/test_orcfile/part-00271</p>
<p> </p>
<p><strong>从下面的ORC文件结构图可以了解相关信息：</strong></p>
<p><strong><br /></strong></p>
<p>我使用下面的命令，将ORC的分析结果输出到了orcfile文件，方便大家查看对照图分析：</p>
<p>hive --orcfiledump /user/hive/warehouse/helloworld.db/test_orcfile/part-00271  &gt; orcfile</p>
<p></p>
<p>从上图中，我们知道在ORC文件中，每个Stripe包括索引数据(IndexData)、行数据(Row Data)及一个Stripe footer。</p>
<p>Stripe footer包含了用于流定位的目录，Row data用于表扫描。</p>
<p>索引数据（Index Data）包括每个列的最小与最大值，以及它们在每个列的行号，行索引项（Row index entries）记录了压缩块及解压后字节的偏移。需要注意的是，ORC索引只是被用来选择Stripe和行组，而不会被用于返回查询结果。拥有相对频繁的行索引条目，可以为了快速的数据读取而跳过一些行，缺省情况下每次最多可以跳过10000行。ORC有能力基于过滤谓词跳过非常多的行，可以使用第二关键字进行对表进行排序，以达到减少查询执行时间的效果。例如，如果主关键字是交易日期，表可以按照省份、邮编号码或者姓名进行排序，当按照省份查询记录的时候将跳过非目标省份的记录。</p>
<p><span style="color:#FF0000;"><br /></span></p>
<p><span style="color:#FF0000;">下面介绍如何在</span><span style="color:#FF0000;">Hive</span><span style="color:#FF0000;">中使用这种存储格式：</span></p>
<p><span style="color:#FF0000;"> </span></p>
<p><strong>1)       支持的数据格式</strong></p>
<ul type="disc"><li>Integer
<ul type="circle"><li>boolean (1 bit) </li><li>tinyint (8 bit) </li><li>smallint (16 bit) </li><li>int (32 bit) </li><li>bigint (64 bit) </li></ul></li><li>Floating point</li></ul><ul type="disc"><li><ul type="circle"><li>float </li><li>double </li></ul></li><li>String types
<ul type="circle"><li>string </li><li>char </li><li>varchar </li></ul></li><li>Binary blobs
<ul type="circle"><li>binary </li></ul></li><li>Date/time
<ul type="circle"><li>timestamp </li><li>date </li></ul></li><li>Compound types
<ul type="circle"><li>struct </li><li>list </li><li>map </li><li>union </li></ul></li></ul><p><strong>2)       Hive DDL</strong></p>
<p>通过指定stored as orc来使用ORC存储格式：</p>
<p>create table orc_table (</p>
<p>id int,</p>
<p>name string</p>
<p>) <strong>stored as orc</strong>;</p>
<p> </p>
<p>可以修改表的存储格式：</p>
<p>alter table simple_table set fileformat orc;</p>
<p>如果simple_table已经存在数据，将导致通过表查询无法访问数据。</p>
<p> </p>
<p> </p>
<p><strong>3)       创建表时，指定ORC存储格式属性</strong></p>
<table border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top">
<p align="center"><strong>KEY</strong></p>
</td>
<td valign="top">
<p align="center"><strong>DEFAULT</strong></p>
</td>
<td valign="top">
<p align="center"><strong>NOTES</strong></p>
</td>
</tr><tr><td valign="top">
<p align="center">orc.compress</p>
</td>
<td valign="top">
<p align="center">ZLIB</p>
</td>
<td valign="top">
<p align="center">high level compression = {NONE, ZLIB, SNAPPY}</p>
<p align="center"><a name="OLE_LINK1">压缩方法</a>(NONE, ZLIB, SNAPPY)</p>
</td>
</tr><tr><td valign="top">
<p align="center">orc.compress.size</p>
</td>
<td valign="top">
<p align="center">262,144</p>
</td>
<td valign="top">
<p align="center">compression chunk size</p>
<p align="center">每个压缩块的字节数</p>
</td>
</tr><tr><td valign="top">
<p align="center">orc.stripe.size</p>
</td>
<td valign="top">
<p align="center">268,435,456</p>
</td>
<td valign="top">
<p align="center">memory buffer size in bytes for writing</p>
<p align="center">每个stripe的字节数</p>
</td>
</tr><tr><td valign="top">
<p align="center">orc.row.index.stride</p>
</td>
<td valign="top">
<p align="center">10,000</p>
</td>
<td valign="top">
<p align="center">number of rows between index entries</p>
<p align="center">索引项之间的行数</p>
</td>
</tr><tr><td valign="top">
<p align="center">orc.create.index</p>
</td>
<td valign="top">
<p align="center">TRUE</p>
</td>
<td valign="top">
<p align="center">create indexes?</p>
<p align="center">是否创建行索引</p>
</td>
</tr><tr><td valign="top">
<p align="center">orc.bloom.filter.columns</p>
</td>
<td valign="top">
<p align="center">""</p>
</td>
<td valign="top">
<p align="center">comma separated list of column names</p>
</td>
</tr><tr><td valign="top">
<p align="center">orc.bloom.filter.fpp</p>
</td>
<td valign="top">
<p align="center">0.05</p>
</td>
<td valign="top">
<p align="center">bloom filter false positive rate</p>
</td>
</tr></tbody></table><p> </p>
<p>比如，创建没有压缩的表：</p>
<p>CREATE TABLE orc_table (</p>
<p>  name STRING,</p>
<p>  age tinyint </p>
<p>) STORED AS ORC TBLPROPERTIES("orc.compress"="NONE");</p>
<p>           </p>
<p><strong>4)       Hive涉及ORC存储文件的配置参数</strong></p>
<p align="left">·        hive.default.fileformat</p>
<p align="left">指定Hive创建表的存储文件格式，默认为TextFile。</p>
<p align="left"> </p>
<p align="left">·        hive.exec.orc.default.compress</p>
<p align="left">ORC的压缩编码方式，默认为ZLIB。</p>
<p align="left"> </p>
<p align="left">·        hive.exec.orc.default.buffer.size</p>
<p align="left">ORC的缓冲大小，默认为262,144(256KB)。</p>
<p align="left"> </p>
<p align="left">·        hive.exec.orc.default.block.size<br />
ORC文件的系统块大小，默认为268,435,456(256MB)</p>
<p align="left"> </p>
<p align="left">·        hive.exec.orc.zerocopy</p>
<p>使用zerocopy读ORC文件。<a title="Hadoop知识库" class="replace_word" href="http://lib.csdn.net/base/20" rel="nofollow" style="color:rgb(223,52,52);font-weight:bold;">Hadoop</a> 2.3以及后续版本支持。</p>
<p> </p>
<p align="left">·        hive.orc.compute.splits.num.threads</p>
<p align="left">ORC使用多少线程去并行化创建分片</p>
<p align="left">hive.exec.orc.skip.corrupt.data         false          </p>
<p align="left">If ORC reader encounters corrupt data, this value will be used todetermine whether to skip the corrupt data or throw an exception.</p>
<p align="left">The default behavioris to throw an exception.</p>
<p align="left"> </p>
<p align="left">·        hive.exec.orc.skip.corrupt.data         </p>
<p align="left">如果ORC读时遇到损坏的数据，此选项决定是否跳过损坏的数据，还是抛出异常。</p>
<p align="left">默认是抛出异常。</p>
<p align="left"> </p>
<p align="left">·        hive.merge.orcfile.stripe.level</p>
<p align="left">当hive.merge.mapfiles,hive.merge.mapredfiles或者hive.merge.tezfiles设置为true时，此时同时以ORC文件格式写表数据，设置此值为true时将快速以stripe级别合并ORC小文件。</p>
<p align="left">·        其他的参数有的用的很少，大家可以参考Hive官网说明进行配置和调优。</p>
<p align="left"><br /></p>
<p align="left"></p>
<h1 id="三dump文件分析"><a name="t2"></a>三、dump文件分析</h1>
<p></p>
<p>　　接下来的分析，请对照着文章<a href="http://blog.csdn.net/dabokele/article/details/51542327">Hive-ORC文件存储格式</a>中的图1-ORC文件结构图进行。
<br />
　　使用hql语句，统计出各字段的count, min, max, sum信息如下：</p>
<table><thead><tr><th align="left">字段</th>
<th align="left">COUNT</th>
<th align="left">MIN</th>
<th align="left">MAX</th>
<th align="left">SUM</th>
</tr></thead><tbody><tr><td align="left">category_id</td>
<td align="left">1000000</td>
<td align="left">5011</td>
<td align="left">975673</td>
<td align="left">4.0222868968E11</td>
</tr><tr><td align="left">product_id</td>
<td align="left">1000000</td>
<td align="left">968</td>
<td align="left">50997770</td>
<td align="left">27158964508399</td>
</tr><tr><td align="left">brand_id</td>
<td align="left">999130</td>
<td align="left">0</td>
<td align="left">1026427</td>
<td align="left">774991825568</td>
</tr><tr><td align="left">price</td>
<td align="left">1000000</td>
<td align="left">-0.0092</td>
<td align="left">358000.0</td>
<td align="left">1.8953626711045265E8</td>
</tr><tr><td align="left">category_id_2</td>
<td align="left">1000000</td>
<td align="left">5010</td>
<td align="left">5996</td>
<td align="left">5.183530839E9</td>
</tr></tbody></table><p>　　 <br />
　　从dump文件的图片中可以看出，大致分成四个部分：</p>
<h2 id="1表结构信息"><a name="t3"></a>1、表结构信息</h2>
<p>　　记录整张表的记录数，压缩方式，压缩大小，以及表结构。在表结构部分，ORC将整张表的所有字段构造成一个大的struct结构。对应图1-ORC文件结构图中的Postscript部分。</p>
<h2 id="２stripe统计信息"><a name="t4"></a>２、Stripe统计信息</h2>
<p>　　统计当前HDFS文件对应Stripe的信息，包括各个字段的count，min, max, sum信息。对于最外层的Struct，只统计其count值。由于这张表数据量不大，当前HDFS文件中只有一个Stripe。对应图1-ORC文件结构图中的Stripe Footer部分。
<br />
　　</p>
<h2 id="3file统计信息"><a name="t5"></a>3、File统计信息</h2>
<p>　　统计内容和第二部分一致，不过这里统计的整张表的每个字段count, min, max, sum信息。对应图1-ORC文件结构图中的FileFooter部分。
<br />
　　这里我们将dump文件中的统计信息，与各字段实际统计信息作对比。通过与上面表格中各字段统计信息对比，发现对于int类型和double类型的字段，min, max, sum的结果都是匹配的。但是对于string类型的字段，仅仅只有min, max统计结果一致，sum的结果不相同。</p>
<h2 id="4stripe详细信息"><a name="t6"></a>4、Stripe详细信息</h2>
<p>　　统计各Stripe的offset，总记录行数等Stripe层次的信息。该Stripe中各字段的Index Data和Row Data，以及每个字段的编码方式。
<br />
　　前面一行Stripe: offset: 3 data: 7847351 rows: 1000000 tail: 132 index: 7936应该也是保存在FileFooter中，后面各个字段统计信息对应图1-ORC文件结构图中的Index Data和Row Data部分。
<br />
　　从dump文件中的数据可以看出，每个字段的ROW_INDEX以及DATA信息是保存在一块连续空间中的，这块文件从offset=3开始。这也说明图1-ORC文件结构图中Row Data区的数据紧随Index Data区数据之后。
<br />
　　Index Data数据统计：</p>
<table><thead><tr><th align="left">起始位置</th>
<th align="left">字段</th>
</tr></thead><tbody><tr><td align="left">3……21</td>
<td align="left">STRUCT</td>
</tr><tr><td align="left">22……1141</td>
<td align="left">category_id</td>
</tr><tr><td align="left">1142……3056</td>
<td align="left">product_id</td>
</tr><tr><td align="left">3057……5135</td>
<td align="left">brand_id</td>
</tr><tr><td align="left">5136……7201</td>
<td align="left">price</td>
</tr><tr><td align="left">7202……7938</td>
<td align="left">category_id_2</td>
</tr></tbody></table><p>　　Row Data数据统计：</p>
<table><thead><tr><th align="left">起始位置</th>
<th align="left">字段</th>
<th>描述</th>
</tr></thead><tbody><tr><td align="left">7939……59887</td>
<td align="left">category_id</td>
<td>字段对应词条int流</td>
</tr><tr><td align="left">59888……59898</td>
<td align="left">category_id</td>
<td>词条长度int流</td>
</tr><tr><td align="left">59899……60989</td>
<td align="left">category_id</td>
<td>字典词条数据</td>
</tr><tr><td align="left">60990……3525432</td>
<td align="left">product_id</td>
<td>实际数据int流</td>
</tr><tr><td align="left">3525433……3527085</td>
<td align="left">brand_id</td>
<td>标识IF NULL的byte流</td>
</tr><tr><td align="left">3527086……5708142</td>
<td align="left">brand_id</td>
<td>实际数据int流</td>
</tr><tr><td align="left">5708143……7855016</td>
<td align="left">price</td>
<td>double类型</td>
</tr><tr><td align="left">7855017……7855212</td>
<td align="left">category_id_2</td>
<td>字段对应词条int流</td>
</tr><tr><td align="left">7855213……7855219</td>
<td align="left">category_id_2</td>
<td>词条长度int流</td>
</tr><tr><td align="left">7855220……7855289</td>
<td align="left">category_id_2</td>
<td>字典词条数据</td>
</tr></tbody></table><p>　　在ORC文件的int类型和string类型保存时，会有一个byte流用于记录字段的某个记录是否为null，根据统计只有brand_id 字段的count值不足100000条，也就是说除了brand_id 字段之外，其他字段中没有null值。所以在上面Row Data表中，只有brand_id有一个对应的IF NULL标识流。一个String类型，会将词条数据保存在字节流中，然后一个int流记录每个词条的长度，另外一个int流用于指定字段某个记录对应字典词条中的哪一个。
<br />
　　 <br />
　　这部分最后记录了每一个字段的存储方式，统计如下</p>
<table><thead><tr><th align="left">字段</th>
<th align="left">类型</th>
<th align="left">存储方式</th>
</tr></thead><tbody><tr><td align="left">STRUCT</td>
<td align="left"> </td>
<td align="left">DIRECT</td>
</tr><tr><td align="left">category_id</td>
<td align="left">String</td>
<td align="left">DICTIONARY_V2</td>
</tr><tr><td align="left">product_id</td>
<td align="left">Int</td>
<td align="left">DIRECT_V2</td>
</tr><tr><td align="left">brand_id</td>
<td align="left">Int</td>
<td align="left">DIRECT_V2</td>
</tr><tr><td align="left">price</td>
<td align="left">Double</td>
<td align="left">DIRECT</td>
</tr><tr><td align="left">category_id_2</td>
<td align="left">String</td>
<td align="left">DICTIONARY_V2</td>
</tr></tbody></table><br />                                    </div>
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